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mMass as a Software Tool for the Annotation of Cyclic Peptide Tandem Mass Spectra

Overview of attention for article published in PLOS ONE, September 2012
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Title
mMass as a Software Tool for the Annotation of Cyclic Peptide Tandem Mass Spectra
Published in
PLOS ONE, September 2012
DOI 10.1371/journal.pone.0044913
Pubmed ID
Authors

Timo H. J. Niedermeyer, Martin Strohalm

Abstract

Natural or synthetic cyclic peptides often possess pronounced bioactivity. Their mass spectrometric characterization is difficult due to the predominant occurrence of non-proteinogenic monomers and the complex fragmentation patterns observed. Even though several software tools for cyclic peptide tandem mass spectra annotation have been published, these tools are still unable to annotate a majority of the signals observed in experimentally obtained mass spectra. They are thus not suitable for extensive mass spectrometric characterization of these compounds. This lack of advanced and user-friendly software tools has motivated us to extend the fragmentation module of a freely available open-source software, mMass (http://www.mmass.org), to allow for cyclic peptide tandem mass spectra annotation and interpretation. The resulting software has been tested on several cyanobacterial and other naturally occurring peptides. It has been found to be superior to other currently available tools concerning both usability and annotation extensiveness. Thus it is highly useful for accelerating the structure confirmation and elucidation of cyclic as well as linear peptides and depsipeptides.

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The data shown below were compiled from readership statistics for 238 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 <1%
Italy 2 <1%
United States 2 <1%
Russia 1 <1%
Netherlands 1 <1%
Unknown 230 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 25%
Student > Master 35 15%
Researcher 34 14%
Student > Bachelor 22 9%
Student > Doctoral Student 18 8%
Other 28 12%
Unknown 42 18%
Readers by discipline Count As %
Chemistry 77 32%
Agricultural and Biological Sciences 42 18%
Biochemistry, Genetics and Molecular Biology 39 16%
Engineering 7 3%
Computer Science 6 3%
Other 17 7%
Unknown 50 21%